import pickle
import pandas as pd
import quandl
import matplotlib.pyplot as plt
from matplotlib import style

style.use("seaborn")

quandl.ApiConfig.api_key = "rFsSehe51RLzREtYhLfo"


def mortgage_30yr():
    df = quandl.get("FMAC/MORTG")
    df = df[df.index > "1974-12-01"]
    df = (df["Value"] - df["Value"][0]) / df["Value"][0] * 100
    df = df.resample("M").mean()
    return df


ax1 = plt.subplot(2, 1, 1)
ax2 = plt.subplot(2, 1, 2, sharex=ax1)

# initial_state_data()

pickle_in = open("fifty_states_pct.pickle", "rb")
HPI_data = pickle.load(pickle_in)

# HPI_Benchmark()

pickle_in = open("us_pct.pickle", "rb")
benchmark = pickle.load(pickle_in)


m30 = mortgage_30yr()

HPI_Bench = benchmark

state_HPI_M30 = HPI_data.join(m30)
state_HPI_M30.rename({"Value": "M30"}, inplace=True)

print(state_HPI_M30.corr().describe()["Value"])
